Sunday January 17, 2010

Winter in the Northern Hemisphere and the impressive jet streams this time of year have seen a few pilots racking up impressive groundspeed numbers. For instance, on Jan 14 CX 744 over the North Pacific (HKG-ANC): 750kts ground speed, with a wind of 217kts (!) almost all of which was tailwind.

3 days earlier a Singapore Airlines 773 recorded GS 725kts, also out over the North Pacific.

Bruce Western (Harvard Sociology) and I will be running a week-long training workshop in quantitative methods through the United States Studies Centre, University of Sydney, 31 May – 4 June, 2010. Details here.

Friday January 15, 2010

A United Airlines plane is stranded at Brisbane airport with an engineering defect, after running low on fuel during an international flight to Sydney.

The United Airlines flight from Los Angeles was due to land in Sydney about 8:00am (AEDT), but instead it was redirected to Brisbane Airport.

…

The passengers have disembarked and are waiting for another United Airlines plane to arrive in Brisbane from Sydney so it can fly them to Sydney Airport.

The UAL aircraft coming from SYD for the pickup would be the aircraft that did SFO-SYD. One wonders about crew hours issues, or maybe they alerted the crew(s) that would have done the return flights today that they would be needed earlier etc?

They were running low on gas? I guess strong headwinds might do that on a westerly Pacific crossing, but this is the first time I’ve heard of it.

Well the aircraft in question was N127UA which also diverted into BNE mid week as well.It was operating UA 839 today .

News Ltd carried the story like this on-line, with what looks like a shot of the aftermath of the UAL wheels-up landing at EWR earlier in the week. That is quite mis-leading. Different airport, different aircraft, very different outcome. But hey, its a “dramatic” photo of a UAL incident…

Saturday January 9, 2010

Out of curiosity, I produced a “sequential” set of ideal point estimate for the (current) 111th U.S. Senate, plotting the results in the graph attached below (click on the thumbnail); as is conventional, red is Republican and blue is Democratic. The analysis uses all 373 non-unanimous roll calls in the 111th Senate thus far.

Each senator starts with a prior centered at zero. Each roll call induces a partitioning of the senators, and a branching in the trajectories of the estimated ideal points, as the space of voting profiles gets richer. We also see the roll calls tending to discriminate among the Republicans more rapidly than the Democrats (the latter being the majority party in the 111th Senate). It is rather striking how quickly we recover a reasonably stable rank-ordering of the senators, at least in 1d in a Congress like that 111th with strong/stable separation along party lines.

Also of interest is the Specter party switch (for a short while the voting history of Specter as a “D” wasn’t especially “D”). Note also that we don’t know much about extremist legislators; until we get votes that have cut-points close to their ideal points, the roll calls just aren’t revealing much about their preferences. Hence we see the trajectories of ideal point estimates of the most liberal senators wavering around a little bit.

I used the doMC package in R to do this on a 4 core machine. Each set of ideal point estimates are based on 4 parallel chains (one on each core), generated using ideal in pscl. The “ideal point estimates” here are Monte Carlo estimates of the mean of the marginal posterior density of each respective ideal points. I used 4 chains (in parallel) 150K iterations each, for each of the 373 non-unanimous roll calls. Even with this many iterations there is a little bit of Monte Carlo error; i.e., with the same priors, and the same voting histories, ideal point estimates should coincide, modulo MC error.

The other thing is that there doesn’t seem to be any obvious “vote 1″ update for ideal points. That is, there is no simple mapping from the ideal point estimate based on m roll call to ideal point estimates based on m+1 roll calls. You have to start the fitting algorithm from scratch each time (and hence the appeal of exploiting multiple cores etc), although the results from the previous run giving pretty good start values.

The character matrix is converted to the corresponding integer matrix
by matching against the dimnames of the array. NA values in any row
of the character matrix are propagated to the result. Unmatched
values result in a subscript out of bounds error. Empty string “” is
not allowed to match and therefore always results in an error.

Monday January 4, 2010

The text is Lohr, 2nd ed. He also references Thomas Lumley’s forthcoming book, Complex Surveys (Wiley 2010), which looks like it will be a super-sized guide to the survey package in R, inter alia. Doug likes the Levy and Lemeshow book on the Stata side. The reading list accompanying the syllabus is interesting too.